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update readme to v2.2
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Expand Up @@ -12,7 +12,7 @@ We are happy to announce that [Xplace 2.0](https://ieeexplore.ieee.org/abstract/

Please check our [TCAD paper](https://ieeexplore.ieee.org/document/10373583) for more details about **Xplace-Route**!

## About
## About Xplace
Xplace is a fast and extensible GPU-accelerated global placement framework developed by the research team supervised by Prof. Evangeline F. Y. Young at The Chinese University of Hong Kong (CUHK). It achieves around 3x speedup per GP iteration compared to DREAMPlace and shows high extensibility.


Expand All @@ -30,37 +30,53 @@ Lixin Liu, Bangqi Fu, Martin D. F. Wong, and Evangeline F. Y. Young. "[Xplace: a

(For the Xplace-NN, please refer to branch [neural](https://github.com/cuhk-eda/Xplace/tree/neural))

## Requirements
- [CMake](https://cmake.org/) >= 3.17
- [GCC](https://gcc.gnu.org/) >= 7.5.0
- [Boost](https://www.boost.org/) >= 1.56.0
- [CUDA](https://developer.nvidia.com/cuda-toolkit) >= 11.3
- [Python](https://www.python.org/) >= 3.8
- [PyTorch](https://pytorch.org/) >= 1.12.0
- [Cairo](https://www.cairographics.org/)
- [Innovus®](https://www.cadence.com/content/cadence-www/global/en_US/home/tools/digital-design-and-signoff/soc-implementation-and-floorplanning/innovus-implementation-system.html) (version 20.14, optional, for detailed routing and design rule checking)

## Setup
1. Clone the Xplace repository. We'll call the directory that you cloned Xplace as `$XPLACE_HOME`.
- [1. How to Build](#1-how-to-build)
* [1.1. Dependencies](#11-dependencies)
- [2. How to Run](#2-how-to-run)
* [2.1. Data Preparation](#21-data-preparation)
* [2.2. Run Placement Flow](#22-run-placement-flow)
* [2.3. Run Your Own Design](#23-run-your-own-design)
* [2.4. Placement Parameters](#24-placement-parameters)
- [3. Other Features](#3-other-features)
* [3.1. GPU-accelerated Place and Global Route Flow](#31-gpu-accelerated-place-and-global-route-flow)
* [3.2. Evaluate the Routability of Placement Solution](#32-evaluate-the-routability-of-placement-solution)
* [3.3. Load Design from Preprocessed File](#33-load-design-from-preprocessed-file)
- [4. Citation](#4-citation)
- [5. Contact](#5-contact)
- [6. License](#6-license)

## 1. How to Build
**Step 1**: Clone the Xplace repository. We'll call the directory that you cloned Xplace as `$XPLACE_HOME`.
```bash
git clone --recursive https://github.com/cuhk-eda/Xplace
```
2. Build the shared libraries used in Xplace.
**Step 2**: Build the shared libraries used in Xplace.
```bash
cd $XPLACE_HOME
mkdir build && cd build
cmake -DPYTHON_EXECUTABLE=$(which python) ..
make -j40 && make install
```

## Prepare data
### 1.1. Dependencies
- [CMake](https://cmake.org/) >= 3.17
- [GCC](https://gcc.gnu.org/) >= 7.5.0
- [Boost](https://www.boost.org/) >= 1.56.0
- [CUDA](https://developer.nvidia.com/cuda-toolkit) >= 11.3
- [Python](https://www.python.org/) >= 3.8
- [PyTorch](https://pytorch.org/) >= 1.12.0
- [Cairo](https://www.cairographics.org/)
- [Innovus®](https://www.cadence.com/content/cadence-www/global/en_US/home/tools/digital-design-and-signoff/soc-implementation-and-floorplanning/innovus-implementation-system.html) (version 20.14, optional, for detailed routing and design rule checking)

## 2. How to Run
### 2.1. Data Preparation
The following script will automatically download `ispd2005`, `ispd2015`, `iccad2019`, `ispd2018`, and `ispd2019` benchmarks in `./data/raw`. It also preprocesses `ispd2015` benchmark to fix some errors. Note that Innovus® can run detailed routing on this fixed `ispd2015`.
```bash
cd $XPLACE_HOME/data
./download_data.sh
```

## Get started
### 2.2. Run Placement Flow
- To run GP + DP flow for ISPD2005 dataset:
```bash
# only run adaptec1
Expand Down Expand Up @@ -104,24 +120,47 @@ In ./result/exp_id
- output # placement solutions
```

## Parameters
Please refer to `main.py`.

## Run custom dataset
You can use the argument `--custom_path` to run your custom LEF/DEF or bookshelf benchmark.
### 2.3. Run Your Own Design
- **Custom Path Mode**: You can use the argument `--custom_path` to run your custom LEF/DEF or bookshelf benchmark.

Suppose there is a LEF/DEF benchmark named `toy` in `data/raw`, you can use the following command line to run the GP + DP flow:
```bash
python main.py --custom_path lef:data/raw/toy_input.lef,def:data/raw/toy_input.def,design_name:toy,benchmark:test --load_from_raw True --detail_placement True
```

You can also use the argument `--custom_json` to run multiple LEFs + DEF + Verilog:
- **Custom JSON Mode**: You can also use the argument `--custom_json` to run multiple LEFs + DEF + Verilog:
```
python main.py --custom_json examples/examples.json --load_from_raw True --target_density 0.9
```
Please provide your LEFs/DEF in the input `json` file. An example of [ASAP7](https://github.com/The-OpenROAD-Project/asap7) input is given in `./examples/examples.json`. Note that the `verilog` format is only partially supported.

## Load design from preprocessed `pt` file (Optional)
### 2.4. Placement Parameters
Please refer to `main.py`.

## 3. Other Features

### 3.1. GPU-accelerated Place and Global Route Flow
Set `--final_route_eval True` in Python arguments to invoke the internal global router [GGR](cpp_to_py/gpugr/README.md) to run GPU-accelerated PnR flow. The flow will output the **placement DEF** and the **global routing guide** in `./result/exp_id/output`. Besides, GR metrics are reported in the log and recorded in `./result/exp_id/log/route.csv`.

- To run Place and Global Route flow for ISPD2015 dataset:
```bash
python main.py --dataset ispd2015_fix --run_all True --load_from_raw True --detail_placement True --use_cell_inflate True --final_route_eval True
```

More details about using GGR in Xplace can be found in [cpp_to_py/gpugr/README.md](cpp_to_py/gpugr/README.md).

### 3.2. Evaluate the Routability of Placement Solution
We provide three ways to evaluate the routability of a placement solution:

1. Set `--final_route_eval True` to invoke [GGR](cpp_to_py/gpugr/README.md) to evaluate the placement solution.

2. Use [CU-GR](https://github.com/cuhk-eda/cu-gr) to evaluate the placement solution by global routing. Please refer to [tool/cugr_ispd2015_fix/README.md](tool/cugr_ispd2015_fix/README.md) for instructions.

3. (Optional). If Innovus® has been properly installed in your OS, you may try to use Innovus® to detailedly route the placement solution. Please refer to [tool/innovus_ispd2015_fix/README.md](tool/innovus_ispd2015_fix/README.md) for instructions.



### 3.3. Load Design from Preprocessed File
The following script will dump the parsed design into a single torch `pt` file so Xplace can load the design from the `pt` file instead of parsing the input file from scratch.

```bash
Expand All @@ -143,27 +182,7 @@ python main.py --dataset ispd2005 --run_all True --load_from_raw False
2. We currently do not support `pt` mode in the routability-driven global placement.
3. If you want to run `pt` mode for the custom dataset, you need to add the custom dataset path in `utils/get_design_params.py`.

## GPU-accelerated place and global route flow (Xplace-Route + GGR)
Set `--final_route_eval True` in Python arguments to invoke the internal global router [GGR](cpp_to_py/gpugr/README.md) to run GPU-accelerated PnR flow. The flow will output the **placement DEF** and the **global routing guide** in `./result/exp_id/output`. Besides, GR metrics are reported in the log and recorded in `./result/exp_id/log/route.csv`.

- To run Place and Global Route flow for ISPD2015 dataset:
```bash
python main.py --dataset ispd2015_fix --run_all True --load_from_raw True --detail_placement True --use_cell_inflate True --final_route_eval True
```

More details about using GGR in Xplace can be found in [cpp_to_py/gpugr/README.md](cpp_to_py/gpugr/README.md).

## Evaluate the routability of Xplace's solution
We provide three ways to evaluate the routability of a placement solution:

1. Set `--final_route_eval True` to invoke [GGR](cpp_to_py/gpugr/README.md) to evaluate the placement solution.

2. Use [CU-GR](https://github.com/cuhk-eda/cu-gr) to evaluate the placement solution by global routing. Please refer to [tool/cugr_ispd2015_fix/README.md](tool/cugr_ispd2015_fix/README.md) for instructions.

3. (Optional). If Innovus® has been properly installed in your OS, you may try to use Innovus® to detailedly route the placement solution. Please refer to [tool/innovus_ispd2015_fix/README.md](tool/innovus_ispd2015_fix/README.md) for instructions.


## Citation
## 4. Citation
If you find **Xplace** useful in your research, please consider to cite:
```bibtex
@article{xplace_tcad,
Expand Down Expand Up @@ -205,15 +224,13 @@ Thanks the authors of [ePlace](https://dl.acm.org/doi/10.1145/2699873), [RePlAce
}
```


## Contact
## 5. Contact

- [Lixin Liu](https://liulixinkerry.github.io/) ([email protected])
- Bangqi Fu ([email protected])
- [Bangqi Fu](https://scholar.google.com/citations?user=TKKJx3gAAAAJ&hl=en) ([email protected])
- [Shiju Lin](https://shijulin.github.io/) ([email protected])
- [Jinwei Liu](https://anticold.github.io/) ([email protected])


## License
## 6. License

Xplace is an open source project licensed under a BSD 3-Clause License that can be found in the [LICENSE](LICENSE) file.

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